Measurement of Passive R, L, and C Components Under Nonsinusoidal Conditions: The Solution of Some Case Studies

This paper deals with the measurement of the R, L, and C parameters of passive components in nonsinusoidal conditions. Since these components usually work with voltage and current waveforms that are different from sinusoidal ones, nonsinusoidal characterization has to be made. The importance of nonsinusoidal characterization of passive components is highlighted through the analysis of two case studies: (1) the influence of distorted waveforms on the line impedance stabilizer network (LISN) passive component behaviors and (2) the influence of voltage and current harmonics on hybrid filter responses. In this paper, the authors propose and describe a measurement method based on linear system identification and model parameter estimation techniques. Then, the two case studies are analyzed and described with the help of some test results

Web-Beagle: a web server for the alignment of RNA secondary structures

Web-Beagle (http://beagle.bio.uniroma2.it) is a web server for the pairwise global or local alignment of RNA secondary structures. The server exploits a new encoding for RNA secondary structure and a substitution matrix of RNA structural elements to perform RNA structural alignments. The web server allows the user to compute up to 10 000 alignments in a single run, taking as input sets of RNA sequences and structures or primary sequences alone. In the latter case, the server computes the secondary structure prediction for the RNAs on-the-fly using RNAfold (free energy minimization). The user can also compare a set of input RNAs to one of five pre-compiled RNA datasets including lncRNAs and 3' UTRs. All types of comparison produce in output the pairwise alignments along with structural similarity and statistical significance measures for each resulting alignment. A graphical color-coded representation of the alignments allows the user to easily identify structural similarities between RNAs. Web-Beagle can be used for finding structurally related regions in two or more RNAs, for the identification of homologous regions or for functional annotation. Benchmark tests show that Web-Beagle has lower computational complexity, running time and better performances than other available methods

COTAN : ScRNA-seq data analysis based on gene co-expression

Estimating the co-expression of cell identity factors in single-cell is crucial. Due to the low efficiency of scRNA-seq methodologies, sensitive computational approaches are critical to accurately infer transcription profiles in a cell population. We introduce COTAN, a statistical and computational method, to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can assess the correlated or anti-correlated expression of gene pairs, providing a new correlation index with an approximate p-value for the associated test of independence. COTAN can evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Similarly to correlation network analysis, it provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions, becoming a new tool to identify cell-identity markers. We assayed COTAN on two neural development datasets with very promising results. COTAN is an R package that complements the traditional single cell RNA-seq analysis and it is available at https://github.com/seriph78/COTAN

Модельные характеристики кардиореспираторной системы высококвалифицированных гребцов на байдарках и каноэ

The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the "modeling by satisfaction of spatial restraints" strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of σ values profoundly influences 3D modeling. We show that the σ values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the program's predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLER's objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance

BEAM web server: A tool for structural RNA motif discovery

Motivation: RNA structural motif finding is a relevant problem that becomes computationally hard when working on high-throughput data (e.g. eCLIP, PAR-CLIP), often represented by thousands of RNA molecules. Currently, the BEAM server is the only web tool capable to handle tens of thousands of RNA in input with a motif discovery procedure that is only limited by the current secondary structure prediction accuracies.Results: The recently developed method BEAM (BEAr Motifs finder) can analyze tens of thousands of RNA molecules and identify RNA secondary structure motifs associated to a measure of their statistical significance. BEAM is extremely fast thanks to the BEAR encoding that transforms each RNA secondary structure in a string of characters. BEAM also exploits the evolutionary knowledge contained in a substitution matrix of secondary structure elements, extracted from the RFAM database of families of homologous RNAs. The BEAM web server has been designed to streamline data pre-processing by automatically handling folding and encoding of RNA sequences, giving users a choice for the preferred folding program. The server provides an intuitive and informative results page with the list of secondary structure motifs identified, the logo of each motif, its significance, graphic representation and information about its position in the RNA molecules sharing it

New Artificial Intelligence Approach to Inclination Measurement Based on MEMS Accelerometer

The article presents a research of angular orientation based on a microelectromechanical system (MEMS) accelerometer by using machine learning (ML) and deep learning (DL) model with architectures of deep neural networks (DNNs). In the industrial environment, artificial intelligence (AI) plays a crucial role in automation which is a potential solution for better performance of inclinometer. This article was carried out to apply this intelligent model on the inertial measurement unit to accomplish the angular position. The experiment shows that the ML model correctly learns the relationship between acceleration and tracking angles via polynomial regression with an R-square of 0.98. The employed DL model with four hidden layers of ten neurons achieves an accuracy of 99.99 % and almost a nonerror performance. The acceleration acquisitions were obtained from MEMS accelerometer LSM9DS1 at a frequency of 50 Hz via microcontroller STM32F401RE. The ML and DNN models were designed based on the platform Tensorflow with high processing accuracy. The Pan-Tilt Unit was used as the angle reference for static and dynamic tests. The traditional technique is used for comparison as well as verification of the proposed models. The DL model has better precision over the ML model due to its high structure level with updating weight and error optimization from the neural network structure. Meanwhile, ML shows more stable results in dynamic circumstances

Yaw/Heading optimization by Machine learning model based on MEMS magnetometer under harsh conditions

The paper's main goal is to accomplish a high accuracy of yaw/heading by Machine Learning approach when the motion range of vehicle/device calibration is limited. The nonlinear Random Forest (RF) Regression with proper training has a high potential to deal with the magnetometer uncertainty before calibration and during iron distortion cases. The proposed solution solely requires the magnetometer without other sensor's support. A Pan Tilt Unit-C46 (PTU-C46) with high precise positioning was used as a reference heading value to label the corresponding magnetic features in the learning model. The proposed approach helps yaw estimation to be carried out under harsh conditions, which resolve many difficulties in orientation tracking since the magnetometer is susceptible to hard iron and soft iron in the environment. In addition, many mechanical devices work only within the specific range and waste their dynamic motion around two axes or more just for calibration. Thus, the research focuses on the level rotation calibration around Z-axis within the restricted range of motion for practical application. The experiment was carried out using a low-cost platform equipped with Micro-Electro-Mechanical System (MEMS) sensors as gyroscope, accelerometer, and magnetometer. The 9 Degree of Freedom (DoF) Madgwick was implemented into the Microcontroller to compare with the proposed model. The sensor fusion can track the yaw value after the level calibration despite various error conduction. The RF model accomplishes a superior result with more stability and more minor error. Under iron disturbance or calibration absence, the ML model still maintains the good tracking command with maximum Mean Square Error of about 0.3°, while the Madgwick is unsuccessful in heading measurement due to huge error in these circumstances

Noise Attenuation on IMU Measurement for Drone Balance by Sensor Fusion

Stability is the key to maintain and control the drone, which is challenged by significant noise from drone motors during operation. The paper presents the Kalman filter and Complementary filter based on the quaternion to optimize drone stability. An exponential moving average (EMA) filter is used to minimize the significant vibration noise inside angular rates. The designed models optimize the misleading data from the Inertial Measurement Unit (IMU) sensor on the drone caused by noise. A real test bench was constructed to verify the proposed methods. An MPU 6050 (triaxial accelerometer and triaxial gyroscope) is equipped with a Racing Drone; then, the sensor data is logged in a MicroSD Card for signal analysis. The results demonstrate that the Complementary filter attenuates variation due to the noise, but it has an issue with drift. On the other hand, the Kalman filter accomplishes more stable output surrounding the drone's balanced point

Enforcement Cybersecurity Techniques: A Lightweight Encryption over the CAN-Bus

The continuous search for network connections outside vehicles has increased the surface of cyber-attacks. Indeed, the automotive companies seem to have neglected the protocols of the networks connecting the various electronic components used in any vehicles. The Controller Area Network (CAN), a protocol designed to minimize latency and transmission errors, governs the internal network of vehicles. One of its main features is to use small frames and to transfer the information unencrypted. This last feature, in particular, makes possible attacks in which an attacker can take remote control of the vehicle by inserting a malicious or manipulated message on the communication channel. The design choices made in the first draft of the standard are, however, what has determined the success of this protocol. The confidentiality of the messages exchanged within this network is nevertheless a goal attainable at a higher level: the study of the structure of the transmitted frames shows how it is possible to hide the critical information passing on the communication channel, that is the bits that identify the units responsible for processing a message and the information carried. Such a solution avoids the possibility of large-scale attacks when a pseudo-random factor is introduced into the encryption: with the same message corresponding to two different encodings on two different vehicles, the breaking of the scheme takes place only after appropriate cryptographic analyses. In this article, we want to introduce an encryption approach of the messages exchanged on CAN-Bus through the technique of randomization. As can be seen from the experimental results obtained, this method seems to have a good response in terms of both efficiency and effectiveness